Viral Sequence Sorting Tools Evaluation

This Rmarkdown file assesses the output of CheckV, DeepVirFinder, Kaiju, VIBRANT, VirSorter, and VirSorter2 on multiple training sets of microbial DNA, primarily from NCBI. Created from fungal, viral, bacterial, archeael, protist, and plasmid DNA sequences

Please reach out to James Riddell (riddell.26@buckeyemail.osu.edu) or Bridget Hegarty (beh53@case.edu) regarding any issues, or open an issue on github.

library(ggplot2)

There were 50 or more warnings (use warnings() to see the first 50)

library(plyr)
library(reshape2)
library(viridis)
library(tidyr)
library(dplyr)
library(readr)
library(data.table)
library(pROC)

Import the file that combines the results from each of the tools from running “combining_tool_output.Rmd”:

viruses <- read_tsv("../IntermediaryFiles/viral_tools_combined.tsv")

── Column specification ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  seqtype = col_character(),
  contig = col_character(),
  checkv_provirus = col_character(),
  checkv_quality = col_character(),
  method.x = col_character(),
  Classified = col_character(),
  IDs_all = col_character(),
  Seq = col_character(),
  Kaiju_Viral = col_character(),
  Kingdom = col_character(),
  type = col_character(),
  vibrant_quality = col_character(),
  method.y = col_character(),
  vibrant_prophage = col_character(),
  vs2type = col_character(),
  max_score_group = col_character()
)
ℹ Use `spec()` for the full column specifications.
There were 29 warnings (use warnings() to see them)

viruses$checkv_completeness[is.na(viruses$checkv_completeness)] <- 0

There were 17 warnings (use warnings() to see them)

This section defines a viralness score “keep_score” based on the tool classifications. A final keep_score above 1 indicates we will keep that sequence and call it viral.

VIBRANT Quality == “High Quality Draft”: +1 Quality == “Medium Quality Draft”: +1 Quality == “Low Quality Draft” & provirus == TRUE: +0.5

Virsorter2 Viral >= 50: +0.5 Viral >= 0.95: +0.5

Virsorter category == 1,2,4,5: +1 category == 3,6: +0.5

DeepVirFinder: Score >= 0.7: +0.5 Score >= 0.9: +0.5

Kaiju: Kaiju_viral = “cellular organisms”: -1 Kaiju_viral = “Viruses”: +1

CheckV If %unknown >= 75: +0.5 Hallmark > 2: +1 viral_genes == 0 and host_genes >= 1: keep_score = 0 If 3*viral_genes <= host_genes: keep_score = 0 If length > 50,000 and hallmark == 0: keep_score = 0

This script produces visualizations of these combined viral scorings and includes ecological metrics like alpha diversity.

You can decide which combination is appropriate for them and only need use the tools appropriate for your data.

getting_viral_set_1 <- function(input_seqs,

There were 34 warnings (use warnings() to see them)

                                include_vibrant=FALSE, 
                                include_virsorter2=FALSE,
                                include_deepvirfinder=FALSE,
                                include_tuning_viral=FALSE,
                                include_tuning_not_viral=FALSE,
                                include_virsorter=FALSE) {
  
  keep_score <- rep(0, nrow(input_seqs))
  
  if (include_vibrant) {
    keep_score[input_seqs$vibrant_quality=="complete circular"] <- keep_score[input_seqs$vibrant_quality=="complete circular"] + 1
    keep_score[input_seqs$vibrant_quality=="high quality draft"] <- keep_score[input_seqs$vibrant_quality=="high quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="medium quality draft"] <- keep_score[input_seqs$vibrant_quality=="medium quality draft"] + 1
    keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$checkv_provirus=="Yes"] <- keep_score[input_seqs$vibrant_quality=="low quality draft" & input_seqs$checkv_provirus=="Yes"] + 0.5
  }
  
  if (include_virsorter2) {
    keep_score[input_seqs$viral>=50] <- keep_score[input_seqs$viral>=50] + 0.5
    keep_score[input_seqs$viral>=95] <- keep_score[input_seqs$viral>=95] + 0.5
  }
  
  if (include_virsorter) {
    keep_score[input_seqs$category==1] <- keep_score[input_seqs$category==1] + 1
    keep_score[input_seqs$category==2] <- keep_score[input_seqs$category==2] + 0.5
    keep_score[input_seqs$category==4] <- keep_score[input_seqs$category==4] + 1
    keep_score[input_seqs$category==5] <- keep_score[input_seqs$category==5] + 0.5
  }
  
  if (include_deepvirfinder) {
    keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.7 & input_seqs$checkv_length<20000] + 0.5
    keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] <- keep_score[input_seqs$score>=0.9 & input_seqs$checkv_length<20000] + 0.5
  }
  
  if (include_tuning_viral) {
    keep_score[input_seqs$Kaiju_Viral=="Viruses"] <- keep_score[input_seqs$Kaiju_Viral=="Viruses"] + 0.5
    keep_score[input_seqs$hallmark>2] <- keep_score[input_seqs$hallmark>2] + 0.5
    keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] <- keep_score[input_seqs$percent_unknown>=75 & input_seqs$checkv_length<50000] + 0.5
    keep_score[input_seqs$percent_viral>=50] <- keep_score[input_seqs$percent_viral>=50] + 0.5
  }
  
  if (include_tuning_not_viral) {
    keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] <- keep_score[input_seqs$Kaiju_Viral=="cellular organisms"] - 1
    keep_score[input_seqs$checkv_host_genes>50 & input_seqs$checkv_provirus=="No"] <- keep_score[input_seqs$checkv_host_genes>50 & input_seqs$checkv_provirus=="No"] - 1
    keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] <- keep_score[input_seqs$checkv_viral_genes==0 & input_seqs$checkv_host_genes>=1] - 1
    keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & input_seqs$checkv_provirus=="No"] <- keep_score[((input_seqs$checkv_viral_genes*3) <= input_seqs$checkv_host_genes) & input_seqs$checkv_provirus=="No"] - 1 # consider accounting for provirus designation
    keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_length>500000 & input_seqs$hallmark<=1] - 1
    keep_score[input_seqs$checkv_completeness<1 & input_seqs$hallmark<=1] <- keep_score[input_seqs$checkv_completeness>1 & input_seqs$hallmark<=1] - 1
    keep_score[input_seqs$checkv_completeness<=75 & input_seqs$checkv_length<=5000] <- keep_score[input_seqs$checkv_completeness<=75 & input_seqs$checkv_length<=5000] - 1 # helped with protist contamination
  }
  
  return(keep_score)
  
}

Assessing performance against the “truth”

note that this is only as accurate as the annotations of the input sequences

this function calculates the precision, recall, and F1 score for each pipeline

assess_performance <- function(seqtype, keep_score) {
  
  truepositive <- rep("not viral", length(seqtype))
  truepositive[seqtype=="virus"] <- "viral"
  
  #make confusion matrix
  confusion_matrix <- rep("true negative", length(keep_score))
  confusion_matrix[truepositive=="viral" & keep_score<=1] <- "false negative"
  confusion_matrix[truepositive=="viral" & keep_score>=1] <- "true positive"
  confusion_matrix[truepositive=="not viral" & keep_score>=1] <- "false positive"
  
  TP <- table(confusion_matrix)[4]
  FP <- table(confusion_matrix)[2]
  TN <- table(confusion_matrix)[3]
  FN <- table(confusion_matrix)[1]
  
  precision <- TP/(TP+FP)
  recall <- TP/(TP+FN)
  F1 <- 2*precision*recall/(precision+recall)
  
  MCC <- (TP*TN-FP*FN)/sqrt(as.numeric(TP+FP)*as.numeric(TP+FN)*as.numeric(TN+FP)*as.numeric(TN+FN))
  
  auc <- round(auc(truepositive, keep_score),4)
  
  #by type metrics
  fungal_FP <- table(confusion_matrix[seqtype=="fungi"])[2]
  protist_FP <- table(confusion_matrix[seqtype=="protist"])[2]
  bacterial_FP <- table(confusion_matrix[seqtype=="bacteria"])[2]
  viral_FN <- table(confusion_matrix[seqtype=="virus"])[1]
  
  performance <- c(precision, recall, F1, MCC, auc, fungal_FP, 
                   protist_FP, bacterial_FP, viral_FN)
  names(performance) <- c("precision", "recall", "F1", "MCC", "AUC", "fungal_FP",
                          "protist_FP", "bacterial_FP", "viral_FN")
  
  return(performance)
}

combination of tools list

combos_list <- data.frame(toolcombo=rep(0, 64),
                          tune_not_viral=rep(0, 64),
                          DVF=rep(0, 64),
                          tune_viral=rep(0, 64),
                          VIBRANT=rep(0, 64),
                          VS=rep(0, 64),
                          VS2=rep(0, 64))
p <- 1

for (i in c(0,1)){
  for (j in c(0,1)){
    for (k in c(0,1)){
      for (l in c(0,1)){
        for (m in c(0,1)){
          for (n in c(0,1)){
            combos_list$toolcombo[p] <- paste(i,j,k,l,m,n)
            combos_list$toolcombo2[p] <- paste(if(i){"tv"}else{"0"},if(j){"DVF"}else{"0"},
                                               if(k){"tnv"}else{"0"},if(l){"VB"}else{"0"},
                                               if(m){"VS"}else{"0"},if(n){"VS2"}else{"0"})
            combos_list$tune_not_viral[p] <- i
            combos_list$DVF[p] <- j
            combos_list$tune_viral[p] <- k
            combos_list$VIBRANT[p] <- l
            combos_list$VS[p] <- m
            combos_list$VS2[p] <- n
            p <- p+1
          }
        }
      }
    }
  }
}

combos_list <- combos_list[-1,]

this function builds a list of all of the combinations that the user wants to test. In this case, we’re comparing the performance of all unique combinations of the six tools.

build_score_list <- function(input_seqs, combos) {
  output <- data.frame(precision=rep(0, nrow(combos)),
                       recall=rep(0, nrow(combos)),
                       F1=rep(0, nrow(combos)),
                       MCC=rep(0, nrow(combos)),
                       AUC=rep(0, nrow(combos)),
                       fungal_FP=rep(0, nrow(combos)),
                       protist_FP=rep(0, nrow(combos)),
                       bacterial_FP=rep(0, nrow(combos)),
                       viral_FN=rep(0, nrow(combos)))
  for (i in 1:nrow(combos)) {
    keep_score <- getting_viral_set_1(input_seqs, include_vibrant = combos$VIBRANT[i],
                                            include_virsorter = combos$VS[i],
                                            include_virsorter2 = combos$VS2[i],
                                            include_tuning_viral = combos$tune_viral[i],
                                            include_tuning_not_viral = combos$tune_not_viral[i],
                                            include_deepvirfinder = combos$DVF[i])
  
    output[i,1:9] <- assess_performance(input_seqs$seqtype, keep_score)
    
    output$toolcombo[i] <- paste(combos$tune_viral[i],combos$DVF[i],
                                 combos$tune_not_viral[i], combos$VIBRANT[i],
                                 combos$VS[i], combos$VS2[i])
  }
  
  output[is.na(output)] <- 0

  return (output)
}

Calculate the performance of each pipeline

accuracy_scores <- data.frame(testing_set_index=rep(0, nrow(combos_list)*10),
                      precision=rep(0, nrow(combos_list)*10),
                       recall=rep(0, nrow(combos_list)*10),
                       F1=rep(0, nrow(combos_list)*10),
                       MCC=rep(0, nrow(combos_list)*10), 
                      AUC=rep(0, nrow(combos_list)*10),
                      fungal_FP=rep(0, nrow(combos_list)*10),
                      protist_FP=rep(0, nrow(combos_list)*10),
                      bacterial_FP=rep(0, nrow(combos_list)*10),
                      viral_FN=rep(0, nrow(combos_list)*10))

accuracy_scores <- cbind(testing_set_index=rep(1, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==1,], combos_list))
for (i in 2:10) {
  accuracy_scores <- rbind(accuracy_scores,
                           cbind(testing_set_index=rep(i, nrow(combos_list)),
                              build_score_list(viruses[viruses$Index==i,], combos_list)))
}

library("stringr")

accuracy_scores$numtools <- str_count(accuracy_scores$toolcombo, "1")
#accuracy_scores <- accuracy_scores[order(accuracy_scores$numtools, decreasing=F),]
accuracy_scores <- accuracy_scores[order(accuracy_scores$MCC, decreasing=F),]
accuracy_scores$toolcombo <- factor(accuracy_scores$toolcombo, levels = unique(accuracy_scores$toolcombo))
accuracy_scores$numtools <- as.factor(accuracy_scores$numtools)

Visualize how the precision, recall, and F1 scores change across pipelines.

pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

There were 17 warnings (use warnings() to see them)

p2 <- ggplot(accuracy_scores, aes(x=toolcombo, y=F1, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("F1 Score")
p2

ggplot(accuracy_scores, aes(x=toolcombo, y=precision, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Precision")

ggplot(accuracy_scores, aes(x=toolcombo, y=recall, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Recall")

ggplot(accuracy_scores, aes(x=precision, y=recall, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Precision") +
  ylab("Recall")

ggplot(accuracy_scores, aes(x=toolcombo, y=abs(precision-recall), 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Precision-Recall")

ggplot(accuracy_scores, aes(x=toolcombo, y=MCC, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("MCC")

ggplot(accuracy_scores, aes(x=toolcombo, y=AUC, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("AUC")

ggplot(accuracy_scores, aes(x=toolcombo, y=fungal_FP, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Fungal False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=protist_FP, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Protist False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=bacterial_FP, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Bacterial False Positives")

ggplot(accuracy_scores, aes(x=toolcombo, y=viral_FN, 
                                  color=numtools, fill=numtools)) +
  geom_point(alpha=0.5) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (tv, DVF, tnv, VB, VS, VS2)") +
  ylab("Viral False Negatives")

write_tsv(accuracy_scores, "20220927_accuracy_scores.tsv")

to do: add in clustering and ordination like in the drinking water R notebook

Experimenting

high precision example

viruses$keep_score_high_precision <- getting_viral_set_1(viruses, include_deepvirfinder = F,

There were 50 or more warnings (use warnings() to see the first 50)

                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_kaiju = T,
                                              include_tuning = T,
                                              include_virsorter = F)

Error in getting_viral_set_1(viruses, include_deepvirfinder = F, include_vibrant = T,  : 
  argument 6 matches multiple formal arguments

viruses$confusion_matrix_high_precision <- "true negative"

There were 14 warnings (use warnings() to see them)

viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision<1] <- "false negative"
viruses$confusion_matrix_high_precision[viruses$seqtype=="virus" & viruses$keep_score_high_precision>=1] <- "true positive"
viruses$confusion_matrix_high_precision[viruses$seqtype!="virus" & viruses$keep_score_high_precision>=1] <- "false positive"

visualizing confusion matrix by taxa

confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype, viruses$Index))

The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_precision, viruses$seqtype,     viruses$Index)). In the next version, this warning will become an error.

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")

pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_recall`. 
2: Unknown or uninitialised column: `confusion_matrix_high_recall`. 
3: Unknown or uninitialised column: `confusion_matrix_high_recall`. 
4: Unknown or uninitialised column: `keep_score_visualize`. 
5: Unknown or uninitialised column: `truepositive`. 

ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=checkv_viral_genes, y=confusion_matrix_high_precision,

Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
2: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
3: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
4: Unknown or uninitialised column: `keep_score_visualize`. 
5: Unknown or uninitialised column: `truepositive`. 
6: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
7: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
8: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
9: Unknown or uninitialised column: `keep_score_visualize`. 
10: Unknown or uninitialised column: `truepositive`. 

                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Viral Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=percent_viral, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Percent Genes Viral") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 ggplot(viruses, aes(x=hallmark, y=confusion_matrix_high_precision,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_boxplot(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

 
ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=confusion_matrix_high_precision,
                   color=confusion_matrix_high_precision)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

viruses_false_positive <- viruses[viruses$confusion_matrix_high_precision=="false positive",]

Warning messages:
1: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
2: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
3: Unknown or uninitialised column: `confusion_matrix_high_MCC`. 
4: Unknown or uninitialised column: `keep_score_visualize`. 
5: Unknown or uninitialised column: `truepositive`. 

viruses_false_negative <- viruses[viruses$confusion_matrix_high_precision=="false negative",]

ggplot(viruses, aes(x=hallmark, y=checkv_viral_genes,
                   fill=checkv_length,
                   color=checkv_length,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Number of Viral Genes") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive, aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses_false_positive[viruses_false_positive$seqtype=="bacteria"], aes(x=hallmark, y=checkv_length,
                   fill=checkv_viral_genes,
                   color=checkv_viral_genes,
                   shape=checkv_provirus)) +
  geom_point(alpha=0.3) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Hallmark Genes") +
  ylab("Contig Length") + 
  facet_wrap(~Kaiju_Viral, scales = "free") +
  coord_flip()

Error:
! Must subset columns with a valid subscript vector.
ℹ Logical subscripts must match the size of the indexed input.
✖ Input has size 43 but subscript `viruses_false_positive$seqtype == "bacteria"` has size 1670.
Backtrace:
 1. ggplot2::ggplot(...)
 3. tibble:::`[.tbl_df`(...)
 4. tibble:::vectbl_as_col_location(...)
 7. vctrs::vec_as_location(j, n, names)
 8. vctrs (local) `<fn>`()
 9. vctrs:::stop_indicator_size(...)

table(viruses$hallmark[viruses$confusion_matrix_high_precision=="false positive"]>0)

FALSE  TRUE 
 4398  2608 

table(viruses$percent_host[viruses$confusion_matrix_high_precision=="false positive"]<50)

FALSE  TRUE 
  855  6151 

high MCC example

viruses$keep_score_high_MCC <- getting_viral_set_1(viruses, include_deepvirfinder = F,

There were 34 warnings (use warnings() to see them)

                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_kaiju = T,
                                              include_tuning = T,
                                              include_virsorter = T)

number of items to replace is not a multiple of replacement lengthUnknown or uninitialised column: `checkV_length`.Unknown or uninitialised column: `checkV_length`.

viruses$confusion_matrix_high_MCC <- "true negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC<1] <- "false negative"
viruses$confusion_matrix_high_MCC[viruses$seqtype=="virus" & viruses$keep_score_high_MCC>=1] <- "true positive"
viruses$confusion_matrix_high_MCC[viruses$seqtype!="virus" & viruses$keep_score_high_MCC>=1] <- "false positive"

visualizing confusion matrix by taxa

confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_MCC, viruses$seqtype, viruses$Index))

The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_MCC, viruses$seqtype, viruses$Index)). In the next version, this warning will become an error.

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")

ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),

There were 14 warnings (use warnings() to see them)

                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()

ggplot(viruses, aes(x=checkv_length, y=keep_score_high_MCC,

Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`. 
2: Unknown or uninitialised column: `truepositive`. 

                   fill=confusion_matrix_high_MCC,
                   color=confusion_matrix_high_MCC)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()

high recall example

viruses$keep_score_high_recall <- getting_viral_set_1(viruses, include_deepvirfinder = T,

There were 50 or more warnings (use warnings() to see the first 50)

                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)

number of items to replace is not a multiple of replacement length

viruses$confusion_matrix_high_recall <- "true negative"

There were 17 warnings (use warnings() to see them)

viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall<1] <- "false negative"
viruses$confusion_matrix_high_recall[viruses$seqtype=="virus" & viruses$keep_score_high_recall>=1] <- "true positive"
viruses$confusion_matrix_high_recall[viruses$seqtype!="virus" & viruses$keep_score_high_recall>=1] <- "false positive"

accuracy:

length(grep("true", viruses$confusion_matrix_high_recall))/nrow(viruses)

[1] 0.9172612

visualizing confusion matrix by taxa

confusion_by_taxa <- melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype, viruses$Index))

The melt generic in data.table has been passed a table and will attempt to redirect to the relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well. To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the namespace like reshape2::melt(table(viruses$confusion_matrix_high_recall, viruses$seqtype,     viruses$Index)). In the next version, this warning will become an error.

colnames(confusion_by_taxa) <- c("confusion_matrix", "seqtype","Index", "count")

pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`. 
2: Unknown or uninitialised column: `truepositive`. 

p2 <- ggplot(confusion_by_taxa, aes(x=count, y=as.factor(Index),
                   fill=confusion_matrix,
                   color=confusion_matrix)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Number of Sequences") +
  ylab("") + 
  facet_wrap(~seqtype, scales = "free") +
  coord_flip()
p2

ggplot(viruses, aes(x=checkv_completeness, y=hallmark,

Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`. 
2: Unknown or uninitialised column: `truepositive`. 

                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Number of Hallmark Genes") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()

ggplot(viruses, aes(x=checkv_completeness, y=keep_score_high_MCC,

Warning messages:
1: Unknown or uninitialised column: `keep_score_visualize`. 
2: Unknown or uninitialised column: `truepositive`. 
3: Unknown or uninitialised column: `keep_score_visualize`. 
4: Unknown or uninitialised column: `truepositive`. 

                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_point(stat="identity", shape=21) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("CheckV Completeness") +
  ylab("Pipeline Viral Score") + 
  facet_wrap(~seqtype) + 
  scale_x_log10()

ggplot(viruses, aes(x=confusion_matrix_high_recall, y=checkv_length,

There were 12 warnings (use warnings() to see them)

                   fill=confusion_matrix_high_recall,
                   color=confusion_matrix_high_recall)) +
  geom_boxplot() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(4)), 0.5),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(4)), 1),
                    labels=c("false negative", "false positive", 
                             "true negative", "true positive")) +
  xlab("Sequence Length (bp)") +
  ylab("Pipeline Viral Score") +
  scale_y_log10()

looking at false negatives

viruses_false_negs <- viruses[(viruses$seqtype=="virus" & viruses$keep_score_high_recall<1),]

looking at protists calling viral

viruses_false_pos_protists <- viruses[(viruses$seqtype=="protist" & viruses$keep_score_high_recall>=1),]

There were 40 warnings (use warnings() to see them)

Visualizing confusion matrix by number of tools

viruses$keep_score_visualize <- viruses$keep_score_high_MCC

There were 32 warnings (use warnings() to see them)

viruses$keep_score_visualize[viruses$keep_score_high_MCC>1] <- "> 1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==1] <- "1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==0.5] <- "0.5"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==0] <- "0"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==-0.5] <- "-0.5"
viruses$keep_score_visualize[viruses$keep_score_high_MCC==-1] <- "-1"
viruses$keep_score_visualize[viruses$keep_score_high_MCC<=-1] <- "< -1"

viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
                                       levels=c("< -1", "-1", "-0.5", "0", "0.5","1", "> 1"))
#viruses$keep_score_visualize <- factor(viruses$keep_score_visualize, 
#                                       labels=c("≤ 0", "≤ 0", "≤ 0", "0.5","1", "> 1"))

levels(factor(viruses$keep_score_visualize))

[1] "< -1" "-0.5" "0"    "0.5"  "1"    "> 1" 

pal <- ggthemes::tableau_color_pal(palette="Tableau 20", type="regular")
ggplot(viruses, aes(x=as.factor(Index),
                   fill=keep_score_visualize, color=keep_score_visualize)) +
  geom_bar(stat="count", position="stack") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16)
  ) +
  scale_color_manual(name = 'Viral Score',
                     values = alpha(c(pal(6)), 1)) +
  scale_fill_manual(name = 'Viral Score',
                     values = alpha(c(pal(6)), 0.5)) +
  xlab("Index") +
  ylab("Sequence Count") +
  facet_wrap(~confusion_matrix_high_MCC, scales = "free")

viruses$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf_vs2 <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_dvf_vs2_vs <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses$keep_score_vb_dvf_vs2_vs_tv <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = T)

viruses$keep_score_vb_dvf_vs2_vs_tv_tnv <- getting_viral_set_1(viruses, include_deepvirfinder = T,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = T,
                                              include_virsorter = T)

Considering how each method contributes to the final prediction

viruses_high <- viruses[viruses$keep_score_vb_dvf_vs2_vs_tv>=1,] 
viruses_high_mod <- viruses_high %>% select(keep_score_vb,keep_score_vb_dvf, 
                                            keep_score_vb_dvf_vs2, keep_score_vb_dvf_vs2_vs, 
                                            keep_score_vb_dvf_vs2_vs_tv, keep_score_vb_dvf_vs2_vs_tv_tnv)
#viruses_high_mod <- apply(viruses_high_mod, c(1,2), function(x) {if (x >= 1) {x <- 1} else {x <- 0}})
viruses_high_mod <- as_tibble(viruses_high_mod)

sm_m <- reshape2::melt(viruses_high_mod)
colnames(sm_m) <- c("method", "viral_score")

sm_m <- sm_m[sm_m$viral_score>0,]

sm_m$score <- sm_m$viral_score

sm_m$score[sm_m$viral_score==0.5] <- "0.5"
sm_m$score[sm_m$viral_score>=1] <- "1"
sm_m$score[sm_m$viral_score>=2] <- "2"
sm_m$score[sm_m$viral_score>=3] <- "3"
sm_m$score[sm_m$viral_score>=4] <- "4"
sm_m$score[sm_m$viral_score>=5] <- "5"

sm_m$score <- factor(sm_m$score, 
                                       levels=c("0.5", "1", "2","3","4","5"))

ggplot(sm_m, aes(x=method, y=score,
                   fill=score)) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_manual(name = 'Number of Methods',
                     values = alpha(c(viridis(6)), 1)) +
  xlab("") +
  ylab("Viral Score") +
  coord_flip()

Considering how each method contributes to the final prediction (high MCC)

viruses$keep_score_vb <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = F,
                                              include_tuning_viral = F,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses$keep_score_vb_tv <- getting_viral_set_1(viruses, include_deepvirfinder = F,
                                              include_vibrant = T,
                                              include_virsorter2 = T,
                                              include_tuning_viral = T,
                                              include_tuning_not_viral = F,
                                              include_virsorter = F)

viruses_high <- viruses[viruses$keep_score_vb_tv>=1,] #uncomment this line if want to use all 6 tools
viruses_high_mod <- viruses_high %>% select(keep_score_vb, 
                                            keep_score_vb_tv)
#viruses_high_mod <- apply(viruses_high_mod, c(1,2), function(x) {if (x >= 1) {x <- 1} else {x <- 0}})
viruses_high_mod <- as_tibble(viruses_high_mod)

sm_m <- reshape2::melt(viruses_high_mod)
colnames(sm_m) <- c("method", "score")

ggplot(sm_m, aes(x=method, y=score,
                   fill=as.factor(score))) +
  geom_bar(stat="identity") +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom"
  ) +
  scale_fill_manual(name = 'Number of Methods',
                     values = alpha(c(viridis(14)), 1)) +
  xlab("Primary Method") +
  ylab("Count of Viral Contigs") +
  coord_flip()

ROC

library(pROC)

viruses$truepositive <- rep(0, nrow(viruses))
viruses$truepositive[viruses$seqtype=="virus"] <- 1

rocobj <- roc(viruses$truepositive, viruses$keep_score)
rocobj_all <- roc(viruses$truepositive, viruses$keep_score_all)
auc <- round(auc(viruses$truepositive, viruses$keep_score),4)
auc_all <- round(auc(viruses$truepositive, viruses$keep_score_all),4)
#create ROC plot
ggroc(rocobj, colour = 'steelblue', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc, ')')) +
  coord_equal()
ggroc(rocobj_all, colour = 'green', size = 2) +
  ggtitle(paste0('ROC Curve ', '(AUC = ', auc_all, ')'))

Sensitivity: The probability that the model predicts a positive outcome for an observation when indeed the outcome is positive. Specificity: The probability that the model predicts a negative outcome for an observation when indeed the outcome is negative.

Comparing behavior of all testing sets combined (clustering analyses)

viral_scores <- matrix(data=0, nrow=nrow(viruses), ncol=nrow(combos_list))
num_viruses <- data.frame(toolcombo=rep(0, nrow(combos_list)),
                          num_viruses=rep(0, nrow(combos_list)))

for (i in 1:nrow(combos_list)) {
  viral_scores[,i] <- getting_viral_set_1(viruses, include_vibrant = combos_list$VIBRANT[i],
                                            include_virsorter = combos_list$VS[i],
                                            include_virsorter2 = combos_list$VS2[i],
                                            include_tuning = combos_list$CheckV[i],
                                            include_kaiju = combos_list$Kaiju[i],
                                            include_deepvirfinder = combos_list$DVF[i])
  
  num_viruses$num_viruses[i] <- table(viral_scores[,i]>=1)[[2]]
  
  num_viruses$toolcombo[i] <- combos_list$toolcombo[i]
  
  num_viruses$toolcombo2[i] <- combos_list$toolcombo2[i]
}

num_viruses$numtools <- str_count(num_viruses$toolcombo, "1")
num_viruses <- num_viruses[order(num_viruses$num_viruses, decreasing=F),]
num_viruses$toolcombo <- factor(num_viruses$toolcombo, levels = unique(num_viruses$toolcombo))
num_viruses$toolcombo2 <- factor(num_viruses$toolcombo2, levels = unique(num_viruses$toolcombo2))
num_viruses$numtools <- as.factor(num_viruses$numtools)

ggplot(num_viruses, aes(x=toolcombo, y=num_viruses, 
                                  color=numtools, fill=numtools)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")

ggplot(num_viruses, aes(x=toolcombo2, y=num_viruses, 
                                  color=numtools, fill=numtools)) +
  geom_point() +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Tool Combination (CV, DVF, KJ, VB, VS, VS2)") +
  ylab("Num Viruses Predicted")

ggplot(num_viruses, aes(x=numtools, y=num_viruses)) +
  geom_boxplot(aes(color=numtools)) +
  geom_point(aes(color=numtools, fill=numtools)) +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14, angle = 90),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  xlab("Number of Tools") +
  ylab("Num Viruses Predicted")

viral_scores_nozeros <- viral_scores[rowSums(viral_scores)>0,]
viral_scores_nozeros <- viral_scores_nozeros + 1
viral_scores_nozeros <- as.data.frame(viral_scores_nozeros)

colnames(viral_scores_nozeros) <- num_viruses$toolcombo2

library(phyloseq)

tooldata <- num_viruses

rownames(tooldata) <- tooldata$toolcombo2

physeq_pooled <- phyloseq(otu_table(viral_scores_nozeros, taxa_are_rows = T),
                                     sample_data(tooldata))

ordination <- phyloseq::ordinate(physeq =physeq_pooled, method = "PCoA", distance = "bray")
phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numtools", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw() +
  geom_label(label=tooldata$toolcombo)

phyloseq::plot_ordination(physeq = physeq_pooled, ordination = ordination,
                          shape="numtools", color="num_viruses") + 
  geom_point(size = 3) +
  theme_bw()

to do: try coloring above based on the F1 scores of the testing set on each combination

bray_dist <- phyloseq::distance(physeq_pooled, method="bray")
clusters <- hclust(dist(bray_dist))
plot(clusters)

myclusters <- cutree(clusters, h=1.1)

names(myclusters[myclusters==1])
names(myclusters[myclusters==2])
names(myclusters[myclusters==3])
names(myclusters[myclusters==4])
names(myclusters[myclusters==5])

myclusters_df <- tibble(combo=names(myclusters),
                            cluster_index=myclusters)

myclusters_df <- separate(myclusters_df, col=combo, into=c("CheckV", "DVF",
                                                            "Kaiju", "VIBRANT",
                                                            "VirSorter", "VirSorter2"),
                          sep=" ", remove = F)


tool_count <- as.data.frame(rbind(table(myclusters_df$CheckV, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$DVF, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$Kaiju, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VIBRANT, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter, myclusters_df$cluster_index)[2,],
                          table(myclusters_df$VirSorter2, myclusters_df$cluster_index)[2,])
                    )

tool_count$method <- c("CheckV", "DVF", "Kaiju", "VIBRANT", "VirSorter", "VirSorter2")

tool_count <- melt(tool_count)

colnames(tool_count) <- c("tool", "cluster_index", "tool_count")

pal <- ggthemes::tableau_color_pal(palette="Tableau 10", type="regular")

ggplot(tool_count, aes(x=cluster_index, y=tool_count,
                   fill=cluster_index,
                   color=cluster_index)) +
  geom_bar(stat="identity") +
  theme_light() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank(),
    legend.position = "bottom",
    axis.text.y=element_text(size=14),
    axis.text.x=element_text(size=14),
    legend.text=element_text(size=12),
    axis.title=element_text(size=16),
  ) +
  scale_fill_manual(name="",
                     values = alpha(rev(pal(6)), 0.5)) +
  scale_color_manual(name="",
                     values = alpha(rev(pal(6)), 1)) +
  xlab("Cluster") +
  ylab("Number of Times in Cluster") + 
  facet_wrap(~tool, scales = "free")